Private AI governance workflow

Logo Proposal Validation Agent

An internal AI-assisted brand governance workflow for logo proposals, designed around deterministic policy checks, retrieved guidance, structured outputs, reviewer-ready responses, and human review.

Internal Systems Internal 2024 - Present

Role

AI Product Architect / Technical Lead

University of Arizona

Tags

AI Review WorkflowHuman-in-the-loopPolicy Retrieval

Problem

Logo and brand proposal approvals were consuming reviewer time because submissions needed policy checks, judgment, drafted replies, and follow-up for unclear cases.

Users

Brand reviewers, marketing stakeholders, campus submitters, and leadership teams interested in faster governed review workflows.

Ownership

  • Owned project inception, architecture, development, publishing, documentation, and stakeholder coordination.
  • Designed the AI review flow from submission intake through approve, deny, ambiguous review request, and clarifying follow-up.
  • Coordinated across development and stakeholder groups as interest grew beyond the original reviewer.

Hard Parts

  • Fine-tuned retrieval quality and data quality so RAG-backed policy guidance became more accurate.
  • Discovered the request form itself had unclear fields and one question people often answered strategically just to move through the form faster.
  • Used findings from the AI workflow to help improve the underlying request process, not just automate review.

Leadership

  • Made the technical architecture decisions and drove the project from conversation to shipped workflow.
  • Wrote documentation and coordinated with stakeholders as the workflow expanded.
  • Kept policy-sensitive decisions human-in-the-loop while increasing confidence in routine cases.

Shipped

  • AI review agent with approve, deny, ambiguous, and clarification paths.
  • RAG and deterministic-policy checks with structured outputs.
  • Channel notifications with approval and denial choices.
  • Clarifying-question flow for ambiguous submissions.
  • Tool calling to aid decisions as the system matured.

Impact

  • Reduced manual review burden and increased stakeholder confidence through iteration.
  • Helped reviewers focus on submissions that actually needed human judgment.
  • Created a reusable governed-review pattern for brand and policy workflows.

Signals

Typical reviewer volume: roughly six submissions per day.Manual review, reply drafting, and outreach previously took about 10 minutes per submission.Clear cases can now move through the workflow in under a minute.

Highlights

  • Mapped the real review problem into approve, deny, clarify, and escalate paths for policy-sensitive decisions.
  • Combined deterministic rules, RAG guidance, LLM classification, and structured outputs.
  • Planned reviewer-ready response drafting and Microsoft Teams Adaptive Cards workflow concepts for adoption inside existing operations.
  • Kept ambiguous or policy-sensitive cases human-in-the-loop.
  • Treated the engagement as a reusable pattern for policy retrieval, AI triage, and governed internal review workflows.

Tools

PythonFastAPIOpenAI APIRAGEmbeddingspgvectorPostgresStructured JSONDeterministic rulesHuman reviewEscalation pathsMicrosoft Teams Adaptive Cards